Evolutionary consequences of seed dormancy
Questions and aims
Germ banks spanning over several generations are ubiquitous characteristics to many species (Evans and Dennehy, Q Rev Biol, 2005) encompassing seed dormancy in plants (Templeton and Levin, Am Nat, 1979; Nunney, Am Nat, 2002; Evans et al, Am Nat, 2007), resting eggs for example in pond sediments of Daphnia (Decaestecker et al, Nature, 2007) and survival of spores in bacteria (e.g., Lennon and Jones, Nat Rev Microbiol, 2011). It has been suggested theoretically (e.g., Cohen, J Theor Biol, 1966; Templeton and Levin, Am Nat, 1979) and shown empirically (e.g., Evans et al, Am Nat, 2007) that adaptation for dormancy is a bet-hedging strategy to magnify the evolutionary effect of “good” years and to dampen the effect of “bad” years, i.e. to buffer environmental variability. Importantly, germ dormancy generates an increase of the effective population size compared to the census size of the observable population by (i) promoting the storage of genetic diversity, and (ii) counter-acting habitat fragmentation by buffering against the extinction of small and isolated populations — a phenomenon known as “temporal rescue effect” (Brown and Kodric-Brown, Ecology, 1977; Honnay et al., Oikos, 2008). Seed banks are also key for the conservation of endangered plant species as a life-history trait modulating habitat fragmentation.
However, we still know little about the evolutionary and genomic consequences of seed dormancy in natural populations: Does natural selection acts at seed dormancy genes ? Which genes are these? Does the selective pressure for dormancy vary across environments and/or depending on coevolution? What are the interactions between seed dormancy and other evolutionary forces and random processes (drift, selection, recombination, migration...)? What are the expected pattern of polymorphism in populations with long term seed banks?
To start answering these questions, we have developed models of coalescence integrating seed bank (the germination rate β following Kaj et al., J Appl Proba, 2001). We have shown that one can estimate seed bank parameters for wild tomato species (S. chilense and S. peruvianum) in a metapopulation model, using ecological data on census size of populations and estimated number of demes (Tellier et al., PNAS, 2011).
We have also derive analytically a model integrating seed banks and varying population size. We derive analytically the allelic frequency spectrum, and illustrate with an ABC approach the bias that ignoring seed banks creates when estimating past demography of a species (Živković and Tellier, Mol Ecol, 2012).
1) Extension of mathematical models of coalescence with seed banks with and without selection (collaboration with Prof. J. Müller at TUM Mathematics).
2) We want to understand how stable is adaptation for seed dormancy over time, and if coevolution can promote dormancy.
We investigate the consequences of seed banks on coalescent trees and expected patterns of polymorphism at neutral and selected loci (Tellier et al. 2011; Živković and Tellier 2012; Koopmann et al. 2017, Heinrich et al. 2018; Živković and Tellier 2018; Tellier 2018) and develop new methods to estimate seed banking from genome data (Sellinger et al. 2020, 2021) and assess if seed banks can coevolve with cooperation (Sellinger et al. 2019). We are also starting to dive into Machine Learning methods to detect seed banks (see our website: www.evogenomics.ai)
A summary of the consequences of seed banks at the genomic level is found in a video of a talk given at the CIRM Luminy France, during the international meeting on "Probability and Biological Evolution" in 2015. The video is here